SingleStore GTM brain

category

market profile

• text: Real-time applications and agents require fresh operational data and low-latency answers, while separate transactional and analytical systems introduce data movement.
• text: The platform serves SQL, vector and other data workloads in managed cloud and self-managed editions.

personas

• title: VP of data engineering or database architect
  pains:

  • Maintaining pipelines and copies between transactional systems and analytics
  • Meeting ingestion, concurrency and query latency requirements at scale
• title: Application or AI engineering leader
  pains:

  • Serving current data to customer-facing applications and agents
  • Combining vector search and operational analytics without slow infrastructure
• title: CTO or platform leader
  pains:

  • Balancing performance, resilience, cloud cost and security
  • Reducing complexity across a growing data stack

icp

• text: Enterprises and SaaS builders with high-volume, low-latency applications in finance, retail, technology, healthcare and marketing.
• text: Organizations operating on AWS, Azure or Google Cloud that need production-grade availability and security.

message pillars

• text: Bring operational and analytical workloads together.
• text: Give applications and agents access to fresh data.
• text: Support open data formats for applications and AI.

objections

• objection: Will the economics work at our scale?
  response: Use the usage estimator and benchmark representative ingestion, concurrency and storage before committing.
• objection: Can we meet enterprise availability and security requirements?
  response: Compare edition-specific SLAs, recovery, private networking and key management against your requirements.
• objection: Is migration and integration too disruptive?
  response: Pilot a bounded workload using compatible drivers and assess integration and migration effort.

competitors

• name: MongoDB
  why: Database platform competing for operational and analytical application workloads.
• name: Snowflake
  why: Cloud data platform competing for analytics and AI workloads.
• name: Firebolt
  why: Low-latency cloud analytics database for data applications.
• name: Oracle
  why: Enterprise database alternative.

influencers

• name: Nikita Shamgunov
  url: https://www.singlestore.com/blog/memsql-live-nikita-shamgunov-on-the-data-engineering-podcast/
  why: Co-founder who discussed the database's origins and architecture.

keywords

• real time analytics database
• HTAP database
• distributed SQL database
• real time AI database
• streaming analytics database
• low latency vector search

voice

• Confident and technical
• Performance-oriented
• Enterprise-focused

signals

• text: A data team is struggling with stale analytics caused by pipelines between transactional and analytical databases.
• text: A company is building customer-facing analytics or AI agents that must query live data at scale.

stack

• SQL
• Apache Iceberg
• AWS
• Azure
• Google Cloud
• MySQL protocol

pricing

motion

channels

• developer trial
• SEO
• events
• cloud partnerships
• sales
• LinkedIn

confidence

Founders

• name: Eric Frenkiel
  title: Founder/CEO
  role: CEO
• name: Nikita Shamgunov
  title: Founder/CTO
  role: CTO
  bio: Partner at KV and Chairman at Singlestore. I founded Singlestore as CTO and took over as CEO in 2017. In this capacity I scaled the company from ~9 to ~40M ARR. Singlestore is a YC11 company that's on the trajectory to go public in 2023. Previously was a senior engineer at Facebook on the infrastructure team and Microsoft on the SQL Server. I'm interested in data, analytics, developer tools, AI, and blockchain technology. I have built an enterprise company, but investing much broader